A microcontroller based system for real-time heart rate estimation from ECG signal

This paper illustrates an algorithm for real time detection QRS complex from ECG signal for computation of heart rate. The algorithm is implemented on a standalone embedded system based on Atmel 89C51 microcontroller. Synthetic ECG is generated using Physionet data through the parallel port (LPT1) of a personal computer and delivered to the embedded system. During an initial training period of first 1500 samples, some amplitude and slope based signatures are learned to form a rule base, which are used for detecting the subsequent QRS regions accurately. An average sensitivity of 97.82% and predictivity of 98.35% respectively are obtained from MIT BIH arrhythmia data. From the detected successive R peak locations heart rate has been computed.

[1]  M. R. Neuman,et al.  QRS wave detection , 2006, Medical and Biological Engineering and Computing.

[2]  Masahiko Okada,et al.  A Digital Filter for the ORS Complex Detection , 1979, IEEE Transactions on Biomedical Engineering.

[3]  Chi-Sang Poon,et al.  Analysis of First-Derivative Based QRS Detection Algorithms , 2008, IEEE Transactions on Biomedical Engineering.

[4]  P.E. Trahanias,et al.  An approach to QRS complex detection using mathematical morphology , 1993, IEEE Transactions on Biomedical Engineering.

[5]  M. Okada.  A digital filter for the QRS complex detection. , 1979, IEEE transactions on bio-medical engineering.

[6]  Gerard Olivar,et al.  FPGA-Based Implementation of an Adaptive Canceller for 50/60-Hz Interference in Electrocardiography , 2007, IEEE Transactions on Instrumentation and Measurement.

[7]  Mak Peng Un,et al.  QRS Recognition with Programmable Hardware , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[8]  R. Orglmeister,et al.  QRS Detection Using Zero Crossing Counts , 2003 .

[9]  D.S. Benitez,et al.  A new QRS detection algorithm based on the Hilbert transform , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[10]  Ashish Shukla,et al.  A fast and accurate FPGA based QRS detection system , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Yukinori Suzuki.  Self-organizing QRS-wave recognition in ECG using neural networks , 1995, IEEE Trans. Neural Networks.

[12]  Ivaturi S. N. Murthy,et al.  Syntactic Approach to ECG Rhythm Analysis , 1980, IEEE Transactions on Biomedical Engineering.

[13]  G.G. Cano,et al.  An approach to cardiac arrhythmia analysis using hidden Markov models , 1990, IEEE Transactions on Biomedical Engineering.

[14]  H. K. Chatterjee,et al.  An FPGA implementation of real-time QRS detection , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).

[15]  W J Tompkins,et al.  Applications of artificial neural networks for ECG signal detection and classification. , 1993, Journal of electrocardiology.